A Hierarchical Prior Mining Approach for Non-local Multi-view Stereo

As a fundamental problem in computer vision, multi-view stereo (MVS) aims at recovering the 3D geometry of the target from a set of 2D images. However, the reconstructed quality is significantly impacted by the presence of low-textured areas. In this paper, we propose a Hierarchical Prior Mining (HPM) framework for non-local multi-view stereo. Different from most existing works dedicated to focusing on local information and only using a single prior, HPM captures non-local structural cues and leverages multi-source priors for geometry recovery. Based on the framework, we first propose HPM-MVS, which obtains precise initial hypotheses through non-local operations, simultaneously constructing a better planar prior model in an HPM framework to further facilitate hypothesis generation. In addition, we futher propose HPM-MVS++, which excavates the structured region information of images and spatial geometric relationships of hypotheses as prior knowledge. Then, it incorporates them into probabilistic graphical models, ultimately deducing two novel multi-view matching costs. This significantly enhances the robustness to challenging situations and improves the completeness of the reconstruction. Experimental results on the ETH3D and Tanks & Temples have verified the superior performance and strong generalization capability of our approach.